Joy of distance learning? How student self-efficacy and emotions relate to social support and school environment

Theoretical background

Due to the COVID-19 pandemic being relatively new to European countries, the scientific literature on the emotional and motivational consequences of distance learning related to pandemics is scarce (DeMatthews et al., 2020). The first results addressing the COVID-19 pandemic focus on the general consequences of school closures (Huber and Helm, 2020a, 2020b), on teachers (Voss and Wittwer, 2020), or on very specific aspects, such as teachers’ knowledge acquisition (König et al., 2020). Students’ perspectives are (except for a survey by Huber and Helm, 2020a, 2020b), to our knowledge, yet to be investigated in depth. For this reason, beyond the literature related to the dependent and independent variables in this study and the first empirical findings regarding the COVID-19 crisis, in this section we refer to the literature regarding the role of student learning at home versus student learning at school.

Homeschooling in non-pandemic circumstances

Previous results from research on “homeschooling” under non-pandemic circumstances and homework (Hagenauer and Oberwimmer, 2019; Kohler, 2011; Trautwein et al., 2006) are relevant as the role of the teachers as well as the role of social support (e.g., by family and peers) is increasingly important in this context. In this regard, research shows that especially the organization of education matters. Martin-Chang et al. (2011), for instance, found that students who received structured homeschooling outperformed public school students in all subjects but that students who received unstructured homeschooling showed statistically significant reduced performances compared to public school students. As distance learning under COVID-19 circumstances is much more a consequence of a closed school than a voluntary decision to be educated at home, students in distance learning might be especially disadvantaged (Helm et al., 2021) because many parents have no time to guide their children during their lessons. This is especially relevant when teachers do not pre-structure the working process (Voss and Wittwer, 2020). Another substantial difference between homeschooled children and children in distance learning during COVID-19 is the number of social contacts. While homeschooled children and publicly schooled children do not differ in number of social contacts (Chatham-Carpenter, 1994), and homeschooled children participate in more extracurricular social activities than publicly schooled children (Ray, 2017), during the COVID-19 pandemic school closure period, all extracurricular activities have been cancelled. Thus, the pandemic poses an exceptional challenge to students’ socioemotional wellbeing.

In addition to research in the scope of private homeschooling, a large number of (correlational) studies investigated the relation between social integration/commitment to the school and emotion/motivation in public schools. Support from family and school has been shown to promote persistence and perseverance as well as self-efficacy (Datu, 2017; Eskreis-Winkler et al., 2014; Strayhorn, 2014).

Research on student emotions: The roles of enjoyment, the joy of learning, and anxiety

Emotions are closely connected to the learning process (Linnenbrink-Garcia et al., 2016; Pekrun, 2009; Schutz and Lanehart, 2002; Steinmayr et al., 2020). They influence several information processing mechanisms, including attention and recall but also other variables relevant to learning, such as self-regulation and motivation (Pekrun, 2009; Pekrun et al., 2002). Additionally, emotions influence academic achievement by modulating engagement (Linnenbrink, 2007; Linnenbrink and Pintrich, 2004). Positive emotions are positively correlated to self-regulated learning (Boekaerts et al., 2000; Carver and Scheier, 1990; Pekrun et al., 2002). Anger and anxiety have been found to be related to avoidance (Carver and Harmon-Jone, 2009) or extrinsic motivation, which, in turn, is related to a lack of engagement (Assor et al., 2005). Moreover, it has been proposed that emotions signal the occurrence of an emotionally relevant event and characterize the nature of the resulting motivation (Assor et al., 1986; Buck, 1988; Frijda, 2003). Regarding the present context of research on learning during the COVID-19 pandemic, Huber and Helm (2020a) reported on a correlation of positive emotions with learning outcomes as well as the student–teacher relationship, but the researchers found no correlation between any of the student outcomes with the quality of the digital lessons. Technical equipment was associated with higher positive emotions, investment, and learning outcomes. A negative correlation of negative emotions with learning outcomes was found (Huber and Helm, 2020a; Steinmayr et al., 2020). Indicators of independence (“I considered it especially challenging to plan my learning on my own”) were associated with learning outcomes, investment, and positive emotions and negatively correlated with negative emotions (Huber et al., 2020).

Research on self-efficacy as student motivation, time for learning, and learning outcome

Following the general expectancy–value model of achievement choices (Eccles, 2005), motivation as an essential precursor of effective learning (including a high time investment) is not only influenced by anticipated joy, task difficulty, and achievement motives but depends on a complex interplay of intrapersonal and environmental factors, including ability, siblings, interpretations of former experiences, gender roles, cultural stereotypes, the attitudes of the social agents, goals, and expectations. One often-investigated construct of expectation is self-efficacy (Bandura, 1977). Self-efficacy refers to the belief in one’s own capability to solve problems or master challenges within a certain area of competence. Therefore, self-efficacy is future-oriented and task-specific (Bong and Skaalvik, 2003). It is acquired through repeated experiences of successful behavior but also the subjectively experienced belief of social agents in one’s own abilities. Self-efficacy fosters the motivation to learn and to develop new skills (Schunk, 1985). In previous studies, self-efficacy was found to be an influential factor for self-regulation and academic achievement (Bandura, 1977; Pekrun and Perry, 2014; Ryan and Deci, 2000) as well as a predictor of mathematics performance (Pajares and Graham, 1999). It has been shown to be influenced by the learning environment through certain positive experiences, reinforcing feedback, verbal encouragement, social persuasion, and adequate task difficulties (Harter, 1978; Usher and Pajares, 2008; Vallerand and Reid, 1988). Self-efficacy is especially important for children at risk to develop a sense of agency. Therefore, teachers should aim at directing students’ attention to their psychological capital (Seligman, 2002), thus supporting the development of self-efficacy, confidence, hope, and resiliency. This belief is the motivational basis of effort (such as the amount of time spent on learning or perseverance) and learning outcomes (such as knowledge or skills), which are typically measured by self-reporting, teacher questionnaires, standardized assessments, and/or school grades.

Quality of (virtual) exchange during digital lessons

Research has shown that an effective student–teacher relationship is correlated with better academic achievement (Graziano et al., 2007) as well as more frequent use of self-regulated learning strategies (Patrick and Ryan, 2005), learning motivation, and interest (Wentzel, 1998). Therefore, positive student–teacher relationships might prevent students from failing (Pianta et al., 1995). A relationship, that fosters educational outcomes, is characterized by students feeling that their teacher sees them and cares about their concerns, that they can entrust their problems to their teacher, and that their teacher appreciates them. This impression must be built up by positive teacher–student interaction and student–student interaction (Allen et al., 2011; Brophy and Good, 1974; Goodwin et al., 2021). Therefore, it is questionable whether a high-quality student–teacher relationship can be preserved during distance learning. The lack of face-to-face personal contact limits the whole range of information that is typically transmitted during the interaction and impedes many informal occasions to talk about personal emotions, experiences, and worries as well as to ask questions (Resnick et al., 2015). In addition, during structured online lessons, it is challenging to introduce a productive culture of questioning and discussing (Kiemer et al., 2015; Walshaw and Anthony, 2008). While the teacher needs to eliminate disturbances by students during virtual meetings, he or she has only a few opportunities to monitor and regulate attention processes or cheating attempts (Rovai, 2007).

Beyond the absence of direct student–teacher interactions, virtual exchange also reduces contact with peers. A large amount of learning in different respects happens in interaction with peers at school. Cartland et al. (2003) found that school life, with its social networks, peer interaction, and opportunities to receive informal help and support with emotional issues is an important resource for adolescents. Additionally, the sense of community or relatedness with the school has been found to influence not only psychosocial wellbeing but also motivation and attitudes (Bateman, 2002; Pretty et al., 1994). Different viewpoints foster lively discussions and widen the scope of one’s own rational horizon. Peers can help each other to understand tasks and problems and share strategies or serve as models for observational learning (Webb et al., 2002). Dropping out of school has been found to be better predicted by social factors than by parental factors (Vitaro et al., 2001). This might be due to the mediating role of social interactions with teachers (Perry et al., 2002) and peers (Webb and Mastergeorge, 2003) for self-regulated learning (Zimmerman, 1989).

Furthermore, in the direct effects of contact with peers on learning, which is frequently investigated in group-work settings (Webb and Palincsar, 1996), socioemotional components play a central role (Bateman, 2002; Battistich and Hom, 1997; Pretty et al., 1994; Rubin et al., 2006; Zins et al., 2004). Eisenberg et al. (1998), for instance, showed that expressive environments are positively correlated with the capacity to understand emotional expressions and to regulate emotions (Eisenberg et al., 1998; Fried, 2010). In line with this finding, it has been proposed that emotion-related classroom activities might foster competent emotion regulation, which, in turn, is associated with student engagement, motivation, and commitment and, consequently, learning outcomes (Fried, 2011). It has been shown that individual and social elements contribute to intrinsic group dynamics in collaborative learning situations. Collaborative work at school offers numerous opportunities for experiencing social challenges and developing strategies for individual self-regulation as well as co-regulation and shared regulation (Järvenoja and Järvela, 2009). Schunk (1999) argued that attempts to influence others in co-regulation serve the aim of co-constructing a learning environment that is cognitively, motivationally, and emotionally satisfying for the individual. Thus, social processes at school are an important component of learning motivation and emotional wellbeing (Thompson and Fine, 1999). Therefore, the absence of group-work settings and other opportunities for social and socioemotional learning might lead to worse understanding, less depth of processing, a reduction in or loss of motivation, and problems in emotion regulation.

Research on supportive aspects and school-related factors

Although the amount of influence of structural conditions, such as family background, is under debate (Kundu, 2017), research has shown that social aspects and a student’s individual pre-conditions play a key role in educational outcomes (Schubert and Becker, 2010; Vieluf et al., 2020). Sociodemographic characteristics, such as status, residential area, and family income, have been shown to be relevant predictors of student learning outcomes, for example, for college readiness and academic development (Duncan and Murnane, 2014; Fruchter et al., 2012; Sharkey, 2013; Schubert and Becker, 2010; Taylor et al., 2004). For instance, parents’ social heritage and educational aspiration are central for children’s educational aspiration and success (Buchmann and Dalton, 2002; Dupriez et al., 2012). Especially after-school learning and homework are influenced by parents’ social background (Hagenauer and Oberwimmer, 2019). Trautwein et al. (2006) considered parent characteristics to be a relevant component of homework quality. In addition, significant others, such as teachers or peers at school, or in other contexts, are taken into account. However, these trajectories are not inevitable. Studies have found that school and high-quality teaching are likely to initiate a contextual change from a disadvantageous social background to an education-oriented expedient one (Aikens and Barbarin, 2008; Downey et al., 2004; Temple et al., 2000).

Presence at school might be one means of reducing structural inequality and buffering the effects of social background (Downey et al., 2004). Downey et al. (2004) considered schools as equalizers that may correct for social disadvantages. Apart from the social isolation and lack of peer learning, distance learning is also challenging due to organizational and contextual caveats. Many families’ houses and apartments are not large enough to offer a quiet space for students to learn, and not every family has enough computers or fast-enough Internet access (Fairlie, 2012: 1–2).

Control–value theory of achievement emotions

Summarizing the results above, Pekrun and Stephens (2009) developed a model that explains the relations of learning and achievement with contextual and individual characteristics (see Figure 1). The model proposes that learning and achievement, which massively depend on motivation, problem-solving, and cognitive resources, are related to emotions, especially activating joy and inhibiting anxiety, which in turn are related to certain appraisals such as self-efficacy beliefs. These appraisals are the result of an interaction of environmental factors, including instruction, autonomy support, feedback and sanctions, and achievement expectations. The environmental factors operate within the social contexts of children, that is, mainly at home and at school. This model illustrates the complex preconditions of successful and healthy learning and at the same time demonstrates the challenge of COVID-19 school closures, especially for those children with a low-income social background.

In the context of COVID-19, the influence of the school as a learning environment is assumed to be reduced. Furthermore, Huber and Helm (2020b: 249) related the amount of learning, especially of less-motivated students, partly to control by the teacher. Thus, support from teachers and other caregivers might be a means of regulating learning behavior in less conscientious students. During school closures, the role of the family becomes pronounced in that students are surrounded by the values and attitudes, cultural peculiarities, and specific role images of their families around the clock. Additionally, they depend on the knowledge and skills of their parents and siblings in terms of technical issues and school content and on the time capacities of the respective family members. These resources are explored as “support by the family” in the present study.

Overall, the school closures associated with the COVID-19 pandemic are likely to pose a severe rupture in students’ school life, including: a massive reduction in social contact; limited access to help; few opportunities to learn in group-work settings; likely reduced student–teacher interaction; and a strong correlation of possibilities in academic development with the family’s social, ethnic, and educational background (Steinmayr et al., 2021). These constraints and inequalities might result in students’ poor emotional and motivational constitution.

The present study

In sum, previous research on the relation between different sources of support, as well as other contextual factors, and emotional and motivational consequences has suggested that the school closures during the COVID-19 pandemic pose a risk for the emotional wellbeing and educational success of students in general and students from less-supportive families in particular (Helm et al., 2021). Although the studies by Huber and Helm (2020a, 2020b), Huber et al. (2020), and Helm et al. (2021), provide an overview of a wide range of school-relevant variables from several perspectives, a thorough understanding of emotional and motivational processes, as well as “buffering mechanisms,” requires more faceted information on students’ perception of the crisis and their emotions, motivations, and contextual help structures. Another limitation of Huber and Helm’s (2020a, 2020b) study is that they used only single items to assess students’ perceptions.

Therefore, we build on that knowledge and aim at providing a more insightful perspective on students’ emotional and motivational perceptions regarding distance learning during the COVID-19 pandemic by systematically analyzing the relations of the various preconditions of learning that were suggested by Pekrun and Stephens (2009). We aim at exploring the consequences of the COVID-19 school closures from a student-centered perspective and at revealing possible strategies for buffering the negative consequences of the crisis. Because the transition to adolescence is an especially demanding period, when isolation from peers can be particularly negative for students’ emotional wellbeing and motivational processes, we focus on secondary education, that is, Grades 5–12 with students aged 10–19 years. The self-report questionnaire covered the students’ emotional and motivational consequences as well as contextual conditions, working styles, and different school-based activities, with most concepts measured as scales of multiple items to cover all possible facets.

The following research questions are addressed:

  • RQ1: How do secondary school students perceive distance learning regarding emotions and self-efficacy?

  • RQ2: How closely does the relation of self-efficacy and emotion in COVID-19 distance learning with contextual circumstances follow the principles of the control–value theory of achievement emotions?

  • RQ3: Which environmental factors are relevant in the context of learning during the COVID-19 school closures?

The control–value theory of achievement emotions (Pekrun and Stephens, 2009) served as a framework for the selection of relevant variables in four central areas (see Table 1).

Table

Table 1. Scales of the present study.

Table 1. Scales of the present study.

Discussion

In the present study, the consequences of the school closures during the COVID-19 crisis with regard to emotion and self-efficacy were investigated. This study focused on an exploration of student feelings and motivation during the first lockdown in spring 2020. As research lacks knowledge regarding students’ perceptions during this period of time, a more detailed focus was on the role of supportive family and school-related factors as well as the perceived virtual exchange during digital lessons.

The first research question addressed the role of emotions in distance learning during COVID-19. The descriptive results showed that general enjoyment was rated higher than fear. This result is consistent with previous studies (Frenzel et al., 2016). However, joy was higher than joy in the calibration sample, and fear was lower than fear in the calibration sample (Frenzel et al., 2016). This might be a first indication of negative emotional consequences of the COVID-19 school lockdown during spring 2020.

In order to answer the second research question, we adapted the control–value theory of achievement emotions (Pekrun and Stephens, 2009) as a framework to investigate the prediction of emotions, self-efficacy, and goal attainment. In the first step, the regression of self-efficacy on the environmental predictors showed a medium-to-high predictive value, with social support being more relevant than the variables of the digital classroom. However, besides support by family and school, activation in digital lessons was significantly related to self-efficacy. In the second model, it was shown that self-efficacy is related to general enjoyment and joy of learning but not fear. Additionally, the environmental factors predicted emotions beyond self-efficacy. Thus, a model including the direct effects of environmental factors and self-efficacy showed better adjusted than just the regression from emotion to self-efficacy. In the context of emotion prediction, student–teacher interaction was significantly relevant in all three models. This corroborates previous findings (Allen et al., 2011; Brophy and Good, 1974; Graziano et al., 2007). Surprisingly, we found a positive relationship between fear and student–teacher relationship. A closer look at the items explains this finding. Student–teacher relationship is partly measured by items that address the role of teachers in caring for students and asking about their wellbeing or mastery of assignments. Taking this content of the items into consideration, it is plausible that teachers pay special attention to students they know to be more “shy” or “anxious” compared to others (Nyborg et al., 2020). This may lead to higher values on the scale regarding student–teacher relationship for anxious students. Interestingly, support by family is only predictive for general enjoyment but not for joy of learning, although students learned at home. This not only indicates that both constructs are worth considering but also that joy of learning rather reflects an emotion that is closely connected to learning and the respective socialization agents (teachers and classmates). The small effect size on the model for fear suggests that this emotion might be better predicted by variables not included in the model (e.g., personal traits or learning experiences). In the third step, a regression model with medium effect size resulted. When emotions alone are included as predictors for self-reported goal attainment, general enjoyment has a statistically significant effect. However, in a model with all predictors included, the direct effects of achievement environments and self-efficacy on goal attainment outperform the effects of emotions. This suggests that, although general enjoyment is a predictor of goal attainment, the greatest portion of the explained variance is actually due to self-efficacy, which in turn leads to general enjoyment (Pekrun and Stephens, 2009).

Concerning research question 3, it was found that for emotional wellbeing (i.e., enjoyment, joy of learning, and absence of fear), the student–teacher relationship was an important environmental factor. However, general enjoyment is also influenced by support from family and activation in digital lessons. Additionally, a central component of wellbeing is self-efficacy, which is also highly correlated to support by family and school as well as activation.

The present study corroborated the theoretical assumption that when students are learning at home, parents and other family members are particularly relevant to supporting learning. With regard to performance level in the theoretical model of Pekrun and Stephens (2009), we investigated the role of students’ perceived goal attainment, general enjoyment, and self-efficacy. Thus, family support is important for emotion and motivation during COVID-19 distance learning. However, learning-related outcomes, such as self-efficacy and goal attainment, are also particularly related to support from school, such as information or relationships and the activation in digital lessons. Thus, although parents partly take on the role of educational agents, the main influence on education-related outcomes remains within the reach of social agents from school (Hattie, 2009). Consistent with the findings in non-pandemic circumstances (Pajares, 2003), support by relatives and support by school authorities were both positively correlated with student self-efficacy. Thus, these contextual conditions indirectly exert their influence through appraisals.

Communication and a good relationship with school agents becomes especially relevant in the COVID-19 crisis, when students are learning at home and are particularly exposed to social inequalities. It has been shown that schools can be an equalizer for developmental and cognitive differences that are induced by social inequalities. Downey et al. (2004) showed that during the summer break from school, the gap between socially advantaged and socially disadvantaged students, which had been reduced after school enrollment, becomes larger again. Thus, in the context of COVID-19, it is particularly important to maintain active communication between students and teachers and classmates to ensure that students feel well-informed and know who they can turn to with their issues and concerns at any point in time.

To sum up, while most previous studies have included responses only from parents and teachers (Helm et al., 2021, Steinmayr et al., 2020), the present study offers detailed insights into the emotional and motivational constitution of students immediately after distance learning. The study results corroborate the existing findings on the capacity of support by relatives and support by school authorities, thus emphasizing the importance of well-structured learning, productive virtual exchange during digital lessons, and support by school authorities. In contrast to Huber and Helm’s (2020b) “school barometer” survey, the majority of the sample in the present study filled out a paper-and-pencil questionnaire that was distributed by a teacher in class. Thus, the sample was less selective in terms of motivation, and students with limited technical resources and competencies could be included.

Limitations and future research

There are some limitations that need to be taken into account when interpreting the present results and addressing future research. At first, the design of the study would suggest considering a multilevel data analysis approach. However, the ICCs calculated for each variable of interest (Table 1) showed that the variance between subjects within schools was not considerably smaller than the variance across schools (ICC < 0.06), and therefore, a multilevel approach could be neglected in this study. However, the findings should be carefully interpreted, taking into account the sample size and number of classes involved in this study. Since the student perspective on motivational and emotional consequences of COVID-19 is still understudied, explicit hypotheses concerning these questions were not formulated. Thus, while the present study was rather exploratory, it offers a good basis for future studies. Although the sample size was small, the correlational inferences are valid within the given range of variable values. In this regard, beyond the considered classroom realities (such as perceived quality of virtual exchange during digital lessons), future studies should include the organization of distance learning in more detail (e.g., the portion of self-paced learning, students’ daily schedule, the amounts of individual and group work). Additionally, the differential moderating effects of gender, age, and socioeconomic status should be considered. Concerning digital classes, future studies should investigate what methods and strategies lead to teaching quality and are successfully transferred into the digital classroom, and which formats are newly developed in response to the different requirements of distance learning and the emotional, motivational, and outcome-related consequences (Voss and Wittwer, 2020). Furthermore, it would be valuable to explore other achievement-related emotional states (such as resignation and depression, boredom, frustration, hope, or pride). Additionally, beyond the student perspective, it is relevant to include teachers’ perspectives, which would allow for a better understanding of differences in academic outcomes, emotions, and motivations.

As the present data are cross-sectional, and the results are mainly based on correlational analyses that considered only one point in time, future research should include follow-up measurement points, for example, by comparing control conditions (classes at home due to quarantine conditions) to classes with in-person schooling. This methodological setting would allow access to data in a quasi-experimental setting (DeMathews et al., 2020) with a control group and a repeated measurement in order to make valid inferences on causal relations. This also allows controlling for the general effects of the crisis, that is, fear of infection or stress due to parents’ economic problems. Thus, further research is needed (DeMathews et al., 2020) to better understand educational processes in a pandemic, covering learning outcomes, motivations, and emotions. At the moment, the authors are conducting another study that allows the sample to be extended to n = 500+ students, which permits (under control of whether the lockdown was in 2020 or 2021) the application of structural equation modeling. Additionally, a teacher questionnaire will offer further insights into student emotions, motivation, and learning outcomes in combination with teaching practice.

Aikens, NL, Barbarin, O (2008) Socioeconomic differences in reading trajectories: The contribution of family, neighborhood, and school contexts. Journal of Educational Psychology 100(2): 235251.
Google Scholar | Crossref | ISI

Allen, JP, Pianta, RC, Gregory, A, et al. (2011) An interaction-based approach to enhancing secondary school instruction and student achievement. Science 333(6045):10341037.
Google Scholar | Crossref | Medline | ISI

Anderman, EM (2002) School effects on psychological outcomes during adolescence. Journal of Educational Psychology 94(4): 795809.
Google Scholar | Crossref | ISI

Anderman, LH (2003) Academic and social perceptions as predictors of change in middle school students’ sense of school belonging. The Journal of Experimental Education 72(1): 522.
Google Scholar | Crossref | ISI

Assor, A, Aronoff, J, Messé, LA (1986) An experimental test of defensive processes in impression formation. Journal of Personality and Social Psychology 50(3): 644650.
Google Scholar | Crossref

Assor, A, Kaplan, H, Kanat-Maymon, Y, et al. (2005) Directly controlling teacher behaviors as predictors of poor motivation and engagement in girls and boys: The role of anger and anxiety. Learning and Instruction 15(5): 397413.
Google Scholar | Crossref | ISI

Bandura, A (1977) Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84(2): 191215.
Google Scholar | Crossref | Medline | ISI

Bateman, HV (2002) Sense of community in the school. In: Fisher, AT, Sonn, CC, Bishop, BJ (eds) Psychological Sense of Community. The Plenum Series in Social/Clinical Psychology. Boston, MA: Springer, 161179.
Google Scholar | Crossref

Battistich, V, Hom, A (1997) The relationship between students’ sense of their school as a community and their involvement in problem behaviors. American Journal of Public Health 87(12): 197201.
Google Scholar | Crossref

Boekaerts, M, Pintric, PR, Zeidner, M (2000) Self-regulation: An introductory overview. In: Boekaerts, M, Pintric, PR, Zeidner, M (eds) Handbook of Self-Regulation. San Diego, CA: Academic Press, 19.
Google Scholar | Crossref

Bong, M, Skaalvik, EM (2003) Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review 15(1): 140.
Google Scholar | Crossref | ISI

Brophy, JE, Good, TL (1974) Teacher–Student Relationships: Causes and Consequences. New York: Holt, Rinehart & Winston.
Google Scholar

Buchmann, C, Dalton, B (2002) Interpersonal influences and educational aspirations in 12 countries: The importance of institutional context. Sociology of Education 75(2): 99122.
Google Scholar | Crossref | ISI

Buck, R (1988) Human Motivation and Emotion. New York: John Wiley & Sons.
Google Scholar

Carillo, C, Assunção Flores, M (2020) COVID-19 and teacher education: A literature review of online teaching and learning practices. European Journal of Teacher Education 43(4): 466487.
Google Scholar | Crossref

Cartland, J, Ruch-Ross, HS, Henry, DB (2003) Feeling at home in one’s school: A first look at a new measure. Adolescence 38(150): 305320.
Google Scholar | Medline

Carver, CS, Harmon-Jones, E (2009) Anger is an approach-related affect: Evidence and implications. Psychological Bulletin 135(2): 183204.
Google Scholar | Crossref | Medline | ISI

Carver, CS, Scheier, MF (1990) Origins and functions of positive and negative affect: A control-process view. Psychological Review 97(1): 1935.
Google Scholar | Crossref | ISI

Chatham-Carpenter, A (1994) Home vs. public schoolers: Differing social opportunities. Home School Researcher 10(1): 1524.
Google Scholar

Csikszentmihalyi, M (1990) Flow: The Psychology of Optimal Experience. New York: Harper Perennial.
Google Scholar

Datu, JAD (2017) Sense of relatedness is linked to higher grit in a collectivist setting. Personality and Individual Differences 105: 135138.
Google Scholar | Crossref

DeMatthews, D, Knight, D, Reyes, P, et al. (2020) From the field: Education research during a pandemic. Educational Researcher 49(6): 398402.
Google Scholar | SAGE Journals

Downey, DB, von Hippel, PT, Broh, BA (2004) Are schools the great equalizer? Cognitive inequality during the summer months and the school year. American Sociological Review 69(5): 613635.
Google Scholar | SAGE Journals | ISI

Duncan, GJ, Murnane, RJ (2014) Restoring Opportunity: The Crisis of Inequality and the Challenge for American Education. Cambridge, MA: Harvard Education Press.
Google Scholar

Dupriez, V, Monseur, C, Van Campenhoudt, M, et al. (2012) Social inequalities of post-secondary educational aspirations: Influence of social background, school composition and institutional context. European Educational Research Journal 11(4): 504519.
Google Scholar | SAGE Journals

Eccles, JS (2005) Subjective task value and the Eccles et al. model of achievement-related choices. In: Elliot, AJ, Dweck, CS (eds) Handbook of Competence and Motivation. New York: Guilford, 105121
Google Scholar

Eisenberg, N, Cumberland, A, Spinrad, TL (1998) Parental socialization of emotion. Psychological Inquiry 9(4): 241273.
Google Scholar | Crossref | Medline | ISI

Eskreis-Winkler, L, Shulman, E, Beal, SA, et al. (2014) The grit effect: Predicting retention in the military, the workplace, school and marriage. Frontiers in Psychology 5(36): 112.
Google Scholar | Medline

Eysenck, MW (1979) Anxiety, learning, and memory: A reconceptualization. Journal of Research in Personality 13(4): 363385.
Google Scholar | Crossref

Fairlie, RW (2012) The effects of home access to technology on computer skills: Evidence from a field experiment. Information Economics and Policy 24(3−4): 243253.
Google Scholar | Crossref

Frenzel, AC, Pekrun, R, Goetz, T, et al. (2016) Measuring teachers’ enjoyment, anger, and anxiety: The Teacher Emotions Scales (TES). Contemporary Educational Psychology 46: 148163.
Google Scholar | Crossref

Fried, L (2010) Understanding and enhancing emotion and motivation regulation strategy use in the classroom. International Journal of Learning 17(6): 115129.
Google Scholar

Fried, L (2011) Teaching teachers about emotion regulation in the classroom. Australian Journal of Teacher Education 36(3): 111.
Google Scholar | Crossref

Frijda, NH (2003) Emotions and hedonic experience. In: Kahnemann, D, Diener, E, Schwarz, N (eds) Well-Being: The Foundations of Hedonic Psychology. New York: Russell Sage Foundation, 190210.
Google Scholar

Fruchter, N, Hester, M, Mokhtar, C, et al. (2012) Is Demography Still Destiny? Neighborhood Demographics and Public High School Students’ Readiness for College in New York City. A Research and Policy Brief. Annenberg Institute for School Reform at Brown University, New York. Available at: https://files.eric.ed.gov/fulltext/ED536677.pdf (accessed 12 May 2021).
Google Scholar

Goodwin, AP, Cho, S-J, Reynolds, D, et al. (2021) Explorations of classroom talk and links to reading achievement in upper elementary classrooms. Journal of Educational Psychology 113(1): 2748.
Google Scholar | Crossref

Graziano, PA, Reavis, RD, Keane, SP, et al. (2007) The role of emotion regulation in children’s early academic success. Journal of School Psychology 45(1): 319.
Google Scholar | Crossref | Medline | ISI

Gröschner, A, Schindler, AK, Holzberger, D, et al. (2018) How systematic video reflection in teacher professional development regarding classroom discourse contributes to teacher and student self-efficacy. International Journal of Educational Research 90(1): 223233.
Google Scholar | Crossref

Hagenauer, G, Oberwimmer, K (2019) Zum Zusammenhang zwischen Hausaufgabenpraxis und Leseleistung: Ergebnisse aus PIRLS 2006, 2011 und 2016. [Concerning the relation of homework practice and reading competency: Results from PIRLS 2006, 2011 and 2016]. In: Wallner-Paschon, C, Itzlinger-Bruneforth, U (eds) Lesekompetenz der 10-Jährigen im Trend – Vertiefende Analysen zu PIRLS. [Reading Competencies of 10-year-olds in Trend. Detailed Analyses. regarding PIRLS]. Graz: Leykam, 221237. [In German]
Google Scholar

Harter, S (1978) Effectance motivation reconsidered. Toward a developmental model. Human Development 21(1): 3464.
Google Scholar | Crossref

Hattie, J (2009) Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. New York: Routledge.
Google Scholar

Helm, C, Huber, S, Loisinger, T (2021) Was wissen wir über das Lernen im Fernunterricht während der COVID-19-Pandemie? Nachweise aus Deutschland, Österreich und der Schweiz [What do we know about learning at teaching in distance during the COVID-19 pandemic? Evidence from Germany, Austria, and Switzerland]. Zeitschrift für Erziehungswissenschaft 175. Epub ahead of print 4 March 2021. DOI: 10.1007/s11618-021-01000-z [In German.]
Google Scholar | Crossref

Huber, SG, Helm, C (2020a) COVID-19 and schooling: evaluation, assessment and accountability in times of crises—Reacting quickly to explore key issues for policy, practice and research with the school barometer. Educational Assessment, Evaluation and Accountability: 237270.
Google Scholar | Crossref

Huber, SG, Helm, C (2020b) Lernen in Zeiten der Corona-Pandemie. Die Rolle familiärer Merkmale für das Lernen von Schüler*innen. Befunde vom Schul-Barometer in Deutschland, Österreich und der Schweiz [Learning in times of the COVID-19 pandemic. The role of family characteristics in students’ learning: Results of the school-barometer in Germany, Austria and Switzerland]. In: Fickermann, D, Edelstein, B (eds) “Langsam vermisse ich die Schule . . ..” Schule während und nach der Corona-Pandemie [“I’m slowly missing school . . .” School during and after the corona pandemic]. Münster: Waxmann, 3760. [In German.]
Google Scholar | Crossref

Huber, SG, Günther, PS, Schneider, N, et al. (2020) COVID-19 und Aktuelle Herausforderungen in Schule und Bildung. Erste Befunde des Schul-Barometers in Deutschland, Österreich und der Schweiz [COVID-19 − Current Challenges for School and Education. First Results of the School-Barometer in Germany, Austria and Switzerland]. Münster, Waxmann. In German.]
Google Scholar

Järvenoja, H, Järvelä, S (2009) Emotion control in collaborative learning situations: Do students regulate emotions evoked by social challenges? British Journal of Educational Psychology 79(3): 463481.
Google Scholar | Crossref | Medline

Kiemer, K, Gröschner, A, Pehmer, AK, et al. (2015) Effects of a classroom discourse intervention on teachers’ practice and students’ motivation to learn mathematics and science. Learning and Instruction 35(1): 94103.
Google Scholar | Crossref

Kohler, B (2011) Hausaufgaben. Ein Überblick über didaktische Überlegungen und empirische Untersuchungen [Homework. An overview of didactic considerations and empirical investigations]. Die Deutsche Schule 103(3): 203218. [In German.]
Google Scholar

König, J, Jäger-Biela, DJ, Glutsch, N (2020) Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education 43(4): 608622.
Google Scholar | Crossref

Kundu, A (2017) Grit and agency: A framework for helping students in poverty to achieve academic greatness. National Youth-At-Risk Journal 2(2): 6980.
Google Scholar | Crossref

Linnenbrink, EA (2007) The role of affect in student learning: A multi-dimensional approach to considering the interaction of affect, motivation, and engagement. In: Schutz, PA, Pekrun, R (eds) Emotions in Education. San Diego, CA: Academic Press, 107124.
Google Scholar | Crossref

Linnenbrink, EA, Pintrich, PR (2004) Role of affect in cognitive processing in academic contexts. In: Yun Dai, D, Sternberg, RJ (eds) Motivation, Emotion, and Cognition: Integrative Perspectives on Intellectual Functioning and Development. Mahwah, NJ: Lawrence Erlbaum, 5787.
Google Scholar

Linnenbrink-Garcia, L, Patall, EA, Pekrun, R (2016) Adaptive motivation and emotion in education: Research and principles for instructional design. Policy Insights from the Behavioral and Brain Sciences 3(2): 228236.
Google Scholar | SAGE Journals

Martin-Chang, S, Gould, ON, Meuse, RE (2011) The impact of schooling on academic achievement: Evidence from homeschooled and traditionally schooled students. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement 43(3): 195202.
Google Scholar | Crossref

Nyborg, G, Mjelve, LH, Edwards, A, et al. (2020) Teachers’ strategies for enhancing shy children’s engagement in oral activities: Necessary, but insufficient? International Journal of Inclusive Education 116. Epub ahead of print 9 January 2020. DOI: 10.1080/13603116.2020.1711538.
Google Scholar | Crossref

Osterman, KF (2000) Students’ need for belonging in the school community. Review of Educational Research 70(3): 323367.
Google Scholar | SAGE Journals | ISI

Pajares, F (1996) Self-efficacy beliefs in academic settings. Review of Educational Research 66(4): 543578.
Google Scholar | SAGE Journals | ISI

Pajares, F (2003) Self-efficacy beliefs, motivation, and achievement in writing: A review of the literature. Reading &Writing Quarterly 19(2): 139158.
Google Scholar | Crossref

Pajares, F, Graham, L (1999) Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology 24(2): 124139.
Google Scholar | Crossref | Medline | ISI

Patrick, H, Ryan, AM (2005) Identifying adaptive classrooms: Dimensions of the classroom social environment. In: Anderson Moore, K, Lippman, LH (eds) What Do Children Need to Flourish? Conceptualizing and Measuring Indicators of Positive Development, Volume 3. Boston, MA: Springer, 271287.
Google Scholar | Crossref

Pekrun, R (1992) The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology 41(4): 359376.
Google Scholar | Crossref | ISI

Pekrun, R (2006) The control–value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review 18(4): 315341.
Google Scholar | Crossref | ISI

Pekrun, R (2009) Global and local perspectives on human affect: Implications of the control–value theory of achievement emotions. In: Wosnitza, M, Karabenick, SA, Efklides, A, et al. (eds) Contemporary Motivation Research: From Global to Local Perspectives. Cambridge, MA: Hogrefe, 97115.
Google Scholar

Pekrun, R, Perry, RP (2014) Control–value theory of achievement emotions. In: Pekrun, R, Linnenbrink-Garcia, L (eds) International Handbook of Emotions in Education. New York: Routledge, 120141.
Google Scholar | Crossref

Pekrun, R, Stephens, EJ (2009) Goals, emotions, and emotion regulation: Perspectives of the control–value theory. Human Development 52(6): 357365.
Google Scholar | Crossref

Pekrun, R, Goetz, T, Titz, W, et al. (2002) Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist 37(2): 91105.
Google Scholar | Crossref | ISI

Perry, NE, VandeKamp, KO, Mercer, LK, et al. (2002) Investigating teacher–student interactions that foster self-regulated learning. Educational Psychologist 37(1): 515.
Google Scholar | Crossref | ISI

Pianta, RC, Steinberg, MS, Rollins, KB (1995) The first two years of school: Teacher–child relationships and deflections in children’s classroom adjustment. Development and Psychopathology 7(2): 295312.
Google Scholar | Crossref | ISI

Pretty, GMH, Andrewes, L, Collett, C (1994) Exploring adolescents’ sense of community and its relationship to loneliness. Journal of Community Psychology 22(4): 346358.
Google Scholar | Crossref | ISI

Ray, B (2017) A review of research on homeschooling and what might educators learn? Pro-Posições 28(2): 85103.
Google Scholar | Crossref

Ream, RK (2005) Toward understanding how social capital mediates the impact of mobility on Mexican American achievement. Social Forces 84(1): 201224.
Google Scholar | Crossref | ISI

Resnick, L, Asterhan, C, Clarke, S (eds) (2015) Socializing Intelligence through Academic Talk and Dialogue. Washington, DC: American Educational Research Association.
Google Scholar | Crossref

Rovai, AP (2007) Facilitating online discussions effectively. The Internet and Higher Education 10(1): 7788.
Google Scholar | Crossref

Rubin, KH, Wojslawowicz, JC, Rose-Krasnor, L, et al. (2006) The best friendships of shy/withdrawn children: Prevalence, stability, and relationship quality. Journal of Abnormal Child Psychology 34(2): 139153.
Google Scholar | Crossref | ISI

Ryan, RM, Deci, EL (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 55(1): 6878.
Google Scholar | Crossref | Medline | ISI

Schubert, F, Becker, R (2010) Social inequality of reading literacy: A longitudinal analysis with cross-sectional data of PIRLS 2001 and PISA 2000 utilizing the pair wise matching procedure. Research in Social Stratification and Mobility 28(1): 109133.
Google Scholar | Crossref | ISI

Schunk, DH (1985) Self-efficacy and classroom learning. Psychology in the Schools 22(2): 208223.
Google Scholar | Crossref

Schunk, DH (1999) Social-self interaction and achievement behavior. Educational Psychologist 34(4): 219227.
Google Scholar | Crossref

Schutz, PA, Lanehart, SL (2002) Emotions in education. Educational Psychologist 37(2): 6768.
Google Scholar | Crossref | ISI

Schwarzer, R, Jerusalem, M (1999) Skalen zur Messung von Lehrer- und Schülereigenschaften [Scales for measuring teacher and student characteristics]. In: Schwarzer, R, Jerusalem, M (eds) Dokumentation der psychometrischen Verfahren im Rahmen der Wissenschaftlichen Begleitung des Modellversuchs Selbstwirksame Schulen [Documentation of the Psychometric Procedures as Part of the Scientific Monitoring of the Pilot Project Self-Effective Schools]. Berlin: Freie Universität Berlin, 1314. [In German.]
Google Scholar

Seligman, ME (2002) Positive psychology, positive prevention, and positive therapy. In Snyder, CR, Lopez, SJ (eds) Handbook of Positive Psychology. Oxford: Oxford University Press, 312.
Google Scholar

Sharkey, P (2013) Stuck in Place: Urban Neighborhoods and the End of Progress toward Racial Equality. Chicago, IL: University of Chicago Press.
Google Scholar | Crossref

South, SJ, Haynie, DL (2004) Friendship networks of mobile adolescents. Social Forces 83(1): 315350.
Google Scholar | Crossref

Stanton-Salazar, RD, Spina, SU (2005) Adolescent peer networks as a context for social and emotional support. Youth & Society 36(4): 379417.
Google Scholar | SAGE Journals | ISI

Steinmayr, R, Lazarides, R, Weidinger, A, et al. (2020) Teaching and Learning during the COVID-19 School Lockdown: Realization and Associations with Parent-Perceived Students’ Academic Outcomes—A Study and Preliminary Overview. OSF preprints. Available at: https://osf.io/r724z (accessed 19 May 2021).
Google Scholar

Strayhorn, TL (2014) What role does grit play in the academic success of black male collegians at predominantly white institutions? Journal of African American Studies 18(1): 110.
Google Scholar | Crossref

Taylor, LC, Clayton, JD, Rowley, SJ (2004) Academic socialization: Understanding parental influences on children’s school-related development in the early years. Review of General Psychology 8(3): 163178.
Google Scholar | SAGE Journals | ISI

Temple, JA, Reynolds, AJ, Miedel, WT (2000) Can early intervention prevent high school dropout? Evidence from the Chicago Child–Parent Centers. Urban Education 35(1): 3156.
Google Scholar | SAGE Journals | ISI

Thompson, L, Fine, GA (1999) Socially shared cognition, affect, and behavior: A review and integration. Personality and Social Psychology Review 3(4): 278302.
Google Scholar | SAGE Journals

Trautwein, U, Lüdtke, O, Schnyder, I, et al. (2006) Predicting homework effort: Support for a domain-specific, multilevel homework model. Journal of Educational Psychology 98(2): 438456.
Google Scholar | Crossref

Usher, EL, Pajares, F (2008) Self-efficacy for self-regulated learning: A validation study. Educational and Psychological Measurement 68(3): 443463.
Google Scholar | SAGE Journals | ISI

Vallerand, RJ, Reid, G (1988) On the relative effects of positive and negative verbal feedback on males’ and females’ intrinsic motivation. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement 20(3): 239250.
Google Scholar | Crossref | ISI

Vieluf, S, Praetorius, AK, Rakoczy, K, et al. (2020) Use-and-Offer-Modelle der Unterrichtseffektivität: Eine kritische Reflexion ihrer theoretischen Konzeption [Use-and-offer-models of instruction effectiveness: A critical reflection of its theoretical conception]. Zeitschrift für Pädagogik 66: 6380. [In German.]
Google Scholar

Vitaro, F, Larocque, D, Janosz, M, et al. (2001) Negative social experiences and dropping out of school. Educational Psychology 21(4): 401415.
Google Scholar | Crossref

Voss, T, Wittwer, J (2020) Unterricht in Zeiten von Corona: Ein Blick auf die Herausforderungen aus der Sicht von Unterrichts-und Instruktionsforschung [Teaching in times of corona: A look at the challenges from the perspective of research on learning and instruction]. Unterrichtswissenschaft 48(1): 601627. [In German.]
Google Scholar | Crossref

Walshaw, M, Anthony, G (2008) The teacher’s role in classroom discourse: A review of recent research into mathematics classrooms. Review of Educational Research 78(3): 516551.
Google Scholar | SAGE Journals | ISI

Webb, NM, Mastergeorge, A (2003) Promoting effective helping behavior in peer-directed groups. International Journal of Educational Research 39(1−2): 7397.
Google Scholar | Crossref

Webb, NM, Palincsar, AS (1996) Group processes in the classroom. In: Berliner, DC, Calfee, RC (eds) Handbook of Educational Psychology. New York: Macmillan, 841873.
Google Scholar

Webb, NM, Farivar, SH, Mastergeorge, AM (2002) Productive helping in cooperative groups. Theory into Practice 41(1): 1320.
Google Scholar | Crossref | ISI

Wentzel, KR (1998) Social relationships and motivation in middle school: The role of parents, teachers, and peers. Journal of Educational Psychology 90(2): 202209.
Google Scholar | Crossref | ISI

Zimmerman, BJ (1989) A social cognitive view of self-regulated academic learning. Journal of Educational Psychology 81(3): 329339.
Google Scholar | Crossref | ISI

Zins, JE, Weissberg, RP, Wang, MC, et al. (eds) (2004) Building Academic Success on Social and Emotional Learning: What Does the Research Say? New York: Teachers College Press.
Google Scholar
Previous post 15 Sumber Bagus Untuk Membeli Kain Secara On-line
Next post Editorial: Education in Europe and the COVID-19 Pandemic